A stepped modeling method for molecular models of multi-mineral shale with different water contents

Abstract

The present invention discloses a stepped modeling method for a multi-mineral shale molecular model with different water contents, including: obtaining shale mineral composition data and kerogen structure data of a research area; optimizing the shale mineral composition data to determine the types of minerals; constructing and optimizing mineral crystal models according to the types of minerals; constructing a kerogen planar model based on the kerogen structure data and performing geometric optimization to obtain a kerogen structure model; modeling the mineral crystal model and the kerogen structure model according to a preset mineral proportion to obtain a multi-mineral shale model without water content; calculating the number of water molecules required for models with different water contents by using the molar mass of the multi-mineral shale model without water content and the molar mass of water molecules; combining the determined number of water molecules with the multi-mineral shale model without water content to obtain multi-mineral shale models with different water contents; and performing structural optimization on the multi-mineral shale models with different water contents to obtain a final multi-mineral shale model with water content.

Description

Technical Field

[0001] The present invention belongs to the technical field of oil and gas field geology, and particularly relates to a stepped modeling method for a multi-mineral shale molecular model with different water contents. Background Art

[0002] The global carbon dioxide content has been increasing year by year, and there is an urgent need for an effective way to control the carbon dioxide content in the atmosphere. As a technology that can effectively sequester carbon dioxide and at the same time significantly improve the shale gas recovery rate and increase the production of a single well, the carbon dioxide enhanced shale gas recovery technology has gradually attracted the attention of relevant scholars globally. Studying the feasibility of carbon dioxide enhanced shale gas recovery in different blocks is an important prerequisite for determining the feasibility of this technology. Molecular simulation is considered one of the many methods for studying the feasibility of this technology. Therefore, establishing a shale molecular model that can effectively and accurately represent the true morphology of shale has become a top priority.

[0003] Currently, the commonly used molecular models can be mainly divided into three types. First is the conventional slit model, which commonly uses an inorganic mineral or kerogen as the model wall to establish a single-pore slit model. The advantage of this model is that it is convenient to establish and can effectively study the influence of different pore diameters on the adsorption characteristics. Second is the pore model established by kerogen, which directly simulates using the pore space of kerogen. The advantage is that objectively it can effectively reflect the rough characteristics of the pores in shale and has a certain degree of authenticity. Finally is the composite shale molecular model established by inorganic minerals and kerogen, which uses the two substances as the inner and outer surfaces of the pores respectively to establish a slit model. This model has the advantage of considering the influence of mineral diversity on the adsorption characteristics compared with the conventional slit model. However, the above models all have certain deficiencies and do not truly reflect the mineral composition ratio of shale minerals, so they cannot reflect the true shale morphology.

[0004] During the drilling and fracturing process, water will inevitably come into contact with the reservoir and enter the shale pores, which will have a certain impact on the adsorption characteristics of shale. Therefore, it is very necessary to consider the water content in the shale molecular model. Although there are already some methods for establishing water-bearing shale molecular models, they usually do not consider the influence of the randomness of the water molecule distribution during the modeling process on the model establishment results. For the same water content, due to the randomness of the modeling, the free space volume in the generated molecular models may not be the same, thus generating unnecessary systematic errors in the simulation results. Therefore, there is an urgent need for a method for establishing a water-bearing shale molecular model that can improve the randomness of the water content modeling process, eliminate the influence of randomness on the subsequent molecular simulation process, and provide an accurate model basis for the subsequent study of shale adsorption characteristics. Summary of the Invention

[0005] To solve the above technical problems, the present invention proposes a stepped modeling method for multi-mineral shale molecular models with different water contents. By using the stepped modeling method, shale molecular models with different water contents that can eliminate the influence of randomness are established, thereby eliminating the influence on the subsequent simulation research of adsorption characteristics.

[0006] To achieve the above object, the present invention provides a stepped modeling method for multi-mineral shale molecular models with different water contents, including:

[0007] Obtain the shale mineral composition data and kerogen structure data of the research area;

[0008] Optimize the shale mineral composition data to determine the mineral species;

[0009] Construct and optimize the mineral crystal model according to the mineral species; construct the kerogen planar model according to the kerogen structure data, and perform geometric optimization on the structure of the kerogen planar model to obtain the kerogen structure model;

[0010] Model the mineral crystal model and the kerogen structure model according to the preset mineral proportion to obtain a multi-mineral shale model without water content;

[0011] Determine the water content gradient of the target water content model, and use the molar mass of the multi-mineral shale model without water content and the molar mass of water molecules to calculate the number of water molecules required for different water content models;

[0012] Adopt a stepped modeling method to combine the determined number of water molecules with the multi-mineral shale model without water content to obtain multi-mineral shale models with different water contents;

[0013] Optimize the structure of the multi-mineral shale models with different water contents to obtain the final multi-mineral shale models with water content.

[0014] According to the stepped modeling method for multi-mineral shale molecular models with different water contents provided by the present invention, the shale mineral composition data includes quartz, calcite, dolomite, illite, and chlorite.

[0015] According to the stepped modeling method for multi-mineral shale molecular models with different water contents provided by the present invention, the method for optimizing the shale mineral composition data to obtain the mineral species includes:

[0016] Optimize and process the shale mineral composition data by statistical methods, remove the mineral components with an inorganic mineral content ratio of less than 10%, and reprocess the optimized data so that the sum of the remaining mineral ratios is 100% to obtain the mineral species.

[0017] According to the step - by - step modeling method of the multi - mineral shale molecular model with different water contents provided by the present invention, the method for re - processing the optimized data to make the sum of the remaining mineral proportions equal to 100% is as follows: Calculate the corrected mass fraction of the optimized data, and adjust the sum of the mineral proportions to 100% according to the corrected mass fraction. The calculation is as follows:

[0018]

[0019] m c1 = m1C

[0020] m c2 = m2C

[0021] m c3 = m3C

[0022] ···

[0023] m cn = m n C

[0024] Among them, C is the correction coefficient; m1 ··· m n is the proportion of different minerals in the shale; m c1 ··· m cn is the proportion of minerals after correction.

[0025] According to the step - by - step modeling method of the multi - mineral shale molecular model with different water contents provided by the present invention, the method for calculating the number of water molecules required for models with different water contents is as follows:

[0026]

[0027] Among them, W c is the water content of the model; M wt is the total molar mass of water molecules required for the model; M is the molar mass of the model; N w is the number of water molecules required; M w is the molar mass of water molecules.

[0028] According to the step - by - step modeling method of the multi - mineral shale molecular model with different water contents provided by the present invention, after calculating the number of water molecules required for models with different water contents, the number of water molecules needs to be rounded off.

[0029] According to the step - by - step modeling method of the multi - mineral shale molecular model with different water contents provided by the present invention, the step - by - step modeling method includes establishing a multi - mineral model of high - water - content shale by further increasing the difference in the number of water molecules to the multi - mineral model of low - water - content shale.

[0030] According to the step-by-step modeling method of the multi-mineral shale molecular model with different water contents provided by the present invention, the method for geometric optimization of the kerogen planar model structure to obtain the kerogen structure model includes: performing geometric optimization on the kerogen planar model in the Geometry optimization function of the Forcite module in the Materials Studio software to reach the stable state of the low-energy configuration. The geometric optimization accuracy is set to Ultra-fine, the smart algorithm is used, the Ewald calculation method is adopted for the electrostatic force, the Atom based calculation method is adopted for the van der Waals force, the charge amount is given by the force field, and the COMPASS force field is adopted.

[0031] Technical effects of the present invention: The present invention discloses a step-by-step modeling method of the multi-mineral shale molecular model with different water contents. Through the step-by-step modeling method, the established multi-mineral shale molecular models with different water contents have good inheritance, and this inheritance fundamentally avoids the uncertainty of the random distribution of water molecules in the process of modeling the multi-mineral shale molecular model with high water content. The water-containing multi-mineral shale molecular model established by this method effectively avoids the uncertainty influence of random modeling on the free space volume of the model, reduces the uncertainty influence in the subsequent simulation process of adsorption characteristics, thereby ensuring the accuracy of the simulation results of adsorption characteristics and the feasibility of the carbon dioxide enhanced shale gas recovery technology. Brief Description of the Drawings

[0032] The drawings constituting a part of this application are used to provide a further understanding of this application. The schematic embodiments of this application and their descriptions are used to explain this application and do not constitute an improper limitation to this application. In the drawings:

[0033] Figure 1 It is a schematic flow chart of a step-by-step modeling method of the multi-mineral shale molecular model with different water contents according to an embodiment of the present invention;

[0034] Figure 2 It is a schematic diagram of the crystal structure of shale inorganic minerals according to an embodiment of the present invention;

[0035] Figure 3 It is a schematic diagram of the planar structure of kerogen according to an embodiment of the present invention;

[0036] Figure 4 It is a schematic diagram of the three-dimensional structure of kerogen according to an embodiment of the present invention;

[0037] Figure 5 It is a multi-mineral shale molecular model according to an embodiment of the present invention;

[0038] Figure 6 It is a schematic diagram of the water molecule distribution in the shale molecular model with different water contents according to an embodiment of the present invention;

[0039] Figure 7 Schematic diagram of the carbon dioxide adsorption capacity of the shale molecular model with different water contents in the embodiments of the present invention;

[0040] Figure 8 Schematic diagram of the methane adsorption capacity of the shale molecular model with different water contents in the embodiments of the present invention. Detailed implementation manners

[0041] It should be noted that, without conflict, the embodiments in the present application and the features in the embodiments may be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0042] It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than here.

[0043] As Figure 1 shown, in this embodiment, a stepped modeling method for a multi-mineral shale molecular model with different water contents is provided, including:

[0044] Step S1: Obtain the kerogen and mineral composition data of the target block through online research and published literature as the basis for modeling the multi-mineral shale molecular model. In this embodiment, the shale in the Longmaxi Formation of the Sichuan Basin is selected as the research object, and the specific mineral composition and kerogen molecular structure of the Longmaxi Formation are obtained through online research. The mineral composition ratio of the Longmaxi Formation is shown in Table 1, and the kerogen molecular formula is C 206 H 158 O 19 N4S4.

[0045] Table 1

[0046]

[0047] Step S2: Considering the modeling complexity, only the inorganic minerals with a mineral proportion of more than 10% are considered in this modeling, and the statistical processing method is used to ensure that the sum of the proportions of all minerals is still 100% after removing some minerals. The specific processing method is as follows. Several minerals required for modeling are obtained through screening, including 5 inorganic minerals: quartz, calcite, dolomite, illite, and chlorite, and the organic mineral is kerogen. The following formula is used to process the proportion data of the 5 minerals to obtain the corrected mass fraction:

[0048]

[0049] m c1 = m1C

[0050] m c2 = m2C

[0051] m c3 = m3C

[0052] ···

[0053] m cn = m n C

[0054] where C is the correction coefficient, dimensionless; m1···m n are the proportions of different minerals in the shale, dimensionless. m c1 ···m cn are the mineral proportions after correction.

[0055] The molar mass of minerals is calculated through the mineral molecular formula to obtain the molar amounts of minerals required for modeling. The relevant data are shown in Table 2.

[0056] Table 2

[0057]

[0058] Step S3: After determining the types and proportions of inorganic minerals required for modeling, the crystal models of inorganic minerals mainly come from the American Mineralogist Crystal Structure Database (AMCSD), and the crystal models are as Figure 2 shown. The kerogen planar model is established using Kingdraw software according to the kerogen information obtained from the investigation, as Figure 3 shown. The Geometry optimization function in the Forcite module of Materials Studio software is used to geometrically optimize the inorganic mineral crystals and the kerogen model to make them reach a stable state of low-energy configuration. The geometric optimization accuracy is set to Ultra-fine, the smart algorithm is used, the Ewald calculation method is adopted for the electrostatic force, the Atombased calculation method is adopted for the van der Waals force, the charge amount is assigned by the force field, and the COMPASS force field is adopted.

[0059] Step S4: Use the Packing function in the Amorphous Cell module of materials studio software, and set the output molecular model density to 2.55 g / cm 3 . The force field settings, electrostatic force and van der Waals force settings are the same as those in Step S3.

[0060] Step S5: The molar mass of the multi-mineral shale molecular model established is 190495.58 g / cm 3 , and the molar mass of water molecules is 18 g / cm 3 . The number of water molecules required for models with different water contents is calculated through the following two formulas, and the results are rounded to the nearest integer:

[0061]

[0062] Among them, W c is the water content of the model, dimensionless; M wt is the total molar mass of water molecules required by the model, g / mol; M is the molar mass of the model, g / mol; N w is the number of water molecules required, pieces; M w is the molar mass of water molecules, g / mol.

[0063] The number of water molecules can be obtained by calculation as shown in Table 3.

[0064] Table 3

[0065]

[0066] Step S6: The establishment process of the multi-mineral shale molecular model with different water contents adopts a stepped modeling idea, which can effectively avoid the result that the models with the same water content may have different free space volumes due to the random distribution of water molecules during the establishment of the water-containing model, thus having a negative impact on the subsequent adsorption simulation results. The specific implementation method of the stepped model establishment idea is that on the basis of calculating the number of water molecules required for the shale molecular model with different water contents in Step S5, only the difference in the number of water molecules is added on the basis of the previous model during the modeling process. For example, when establishing the shale model with a water content of 0.2%, 22 water molecules are randomly distributed in the model on the basis of the shale model with a water content of 0%. When establishing the shale model with a water content of 0.4%, 20 water molecules are randomly distributed in the model on the basis of the shale model with a water content of 0.2%, and so on. The number of water molecules required for each step of the stepped establishment idea is shown in Table 4.

[0067] Table 4

[0068]

[0069] The Adsorption Locator module in Materials studio software is used for model establishment, and the Simulated annealing method is used with the precision set to Ultra-fine. The method for setting the force field, electrostatic force and van der Waals force is the same as that in Step S3. The schematic diagram of the water molecule distribution in the established water-containing model is as Figure 6 shown. It can be seen from the figure that the distribution of water molecules in the model has good inheritance from a water content of 0% to 1.5%, avoiding the influence of random distribution on the model establishment result.

[0070] Step S7: To reduce the energy of the established water-containing model to its most stable structure, use the Geometry optimization function in the Forcite module of Materials Studio software to optimize the geometric structures of the established shale molecular models with different water contents. The optimization accuracy is set to Ultra-fine, the method is set to Smart, and the force field settings, electrostatic force, and van der Waals force settings are the same as those in Step S3.

[0071] Through the operations of the above Steps S1 to S7, multi-mineral shale molecular models with different water contents can be obtained. On the one hand, this model well considers the characteristics of the diversity of shale mineral compositions. Different minerals have different effects on adsorption characteristics, and this model well considers the actual mineral composition of shale. On the other hand, in the process of establishing different water contents, this model well avoids the uncertain influence of the random distribution of water molecules on the model establishment results by adopting the idea of stepped modeling, providing better objective model conditions for subsequent adsorption characteristics and feasibility simulations.

[0072] Based on the established shale molecular models with different water contents, the Sorption module in Materials Studio software was used to study the adsorption characteristics of the models. To achieve the balance between simulation accuracy and timeliness, the simulation accuracy was set to Fine, the simulation force field in the whole study was uniformly set to the COMPASS force field, and the charge number of each atom in the molecular model was assigned by the force field. The electrostatic force was determined according to the Ewald method, the van der Waals force was determined according to the Atombased method, and the common GCMC method was used to study the adsorption characteristics of the multi-mineral shale molecular models under the same temperature, different water contents, and pressure conditions.

[0073] In this simulation, the pressure change range was set to 10 - 80 Mpa, and the temperature was set to 373 K. The pressure was converted into the fugacity data at a specific temperature required by the software through the P-R equation for the simulation of the scheme. Under the same simulation conditions, two simulation schemes of CO2 and CH4 were respectively set to explore the differences in the adsorption characteristics of CO2 and CH4 in the shale molecular models with different water contents.

[0074] Based on the model simulation results established by the present invention as shown in Figure 7 and Figure 8 It can be clearly seen from Figure 7 that with the increase in water content, the adsorption amount of CO2 in the model decreased significantly. Figure 8Similar conclusions can also be drawn from this, and the increase in water content also has a negative impact on the CH4 adsorption capacity. From the above two figures, it can be seen that in the model established in this patent, the CO2 adsorption capacity is significantly greater than the CH4 adsorption capacity under the same water content condition. This conclusion plays an important role in verifying the feasibility of the carbon dioxide enhanced shale gas recovery technology for a specific shale formation.

[0075] The present invention discloses a stepped modeling method for a multi-mineral shale molecular model with different water contents. Through the stepped modeling method, the established multi-mineral shale molecular models with different water contents have good inheritance, and this inheritance fundamentally avoids the uncertainty of the random distribution of water molecules during the modeling process of the multi-mineral shale molecular model with water content. The water-containing multi-mineral shale molecular model established by this method effectively avoids the uncertainty influence of random modeling on the free space volume of the model, reduces the uncertainty influence during the subsequent adsorption characteristic simulation process, thereby ensuring the accuracy of the adsorption characteristic simulation results and the feasibility of the carbon dioxide enhanced shale gas exploitation technology.

[0076] The above is only a preferred specific embodiment of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed in the present application should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A stepped modeling method for molecular models of multi-mineral shale with different water contents, characterized in that Including: Obtaining shale mineral composition data and kerogen structure data of the research area; Optimizing the shale mineral composition data to determine mineral species; Constructing an optimized mineral crystal model according to the mineral species; constructing a kerogen planar model according to the kerogen structure data, and performing geometric optimization on the structure of the kerogen planar model to obtain a kerogen structure model; Modeling the mineral crystal model and the kerogen structure model according to a preset mineral proportion to obtain a shale multi-mineral model without water content; Determining the water content gradient of the target water content model, and using the molar mass of the shale multi-mineral model without water content and the molar mass of water molecules to calculate the number of water molecules required for different water content models; Adopting a step-by-step modeling method, combining the determined number of water molecules with the shale multi-mineral model without water content to obtain shale multi-mineral models with different water contents; the step-by-step modeling method includes establishing a high water content shale multi-mineral model by further increasing the difference in the number of water molecules to a low water content shale multi-mineral model; Performing structure optimization on the shale multi-mineral models with different water contents to obtain a final water content shale multi-mineral model.

2. The stepwise modeling method of the multi-mineral shale molecular model with different water contents according to claim 1, wherein, The shale mineral composition data includes quartz, calcite, dolomite, illite and chlorite.

3. The stepped modeling method for the multi-mineral shale molecular model with different water contents as described in claim 1, characterized in that, The method for optimizing the shale mineral composition data to obtain mineral species includes: Optimizing and processing the shale mineral composition data through a statistical method, removing mineral components with an inorganic mineral content ratio of less than 10%, and reprocessing the optimized data so that the sum of the remaining mineral ratios is 100% to obtain mineral species.

4. The stepped modeling method for the multi-mineral shale molecular model with different water contents as described in claim 3, characterized in that The method for reprocessing the optimized data so that the sum of the remaining mineral ratios is 100% is: calculating the corrected mass fraction of the optimized data, and adjusting the sum of the mineral ratios to 100% according to the corrected mass fraction. The calculation is as follows: m c1 = m1C m c2 = m2C m c3 = m3C ··· m cn = m n C Among them, C is the correction coefficient; m1···m n is the proportion of different minerals in the shale; m c1 ···m cn is the proportion of minerals after correction.

5. The stepwise modeling method of the multi-mineral shale molecular model with different water contents as described in claim 1, characterized in that, The method for calculating the number of water molecules required for different water content models is: Among them, W c is the water content of the model; M wt is the total molar mass of water molecules required by the model; M is the molar mass of the model; N w is the number of water molecules required; M w is the molar mass of water molecules.

6. The stepped modeling method for the multi-mineral shale molecular model with different water contents according to claim 1, characterized in that, After calculating the number of water molecules required for different water content models, the number of water molecules needs to be rounded off.

7. The stepped modeling method for the multi-mineral shale molecular model with different water contents as described in claim 1, characterized in that, The method for performing geometric optimization on the structure of the kerogen planar model to obtain a kerogen structure model includes: performing geometric optimization on the kerogen planar model in the Geometry optimization function of the Forcite module in the Materials Studio software to reach a stable state of a low energy configuration, setting the geometric optimization accuracy to Ultra-fine, using the smart algorithm, adopting the Ewald calculation method for the electrostatic force, adopting the Atom based calculation method for the van der Waals force, and the charge amount is given by the force field, and adopting the COMPASS force field.

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